New paper from Basis' Project MARA team and collabs. The ability to learn and use world models is a key aspect of human intelligence, but evaluating this ability remains elusive. In this work we propose WorldTest, a representation-agnostic, behavior-based agent eval framework.
🌆🐀Scientific American article about the project we're starting on NYC rats!! https://t.co/f4kquZ0qaH
With @ralph_ep, @zamakany, and Dima Batenkov
Seeking collaborators and funders 🐀🌆
New preprint!
tl;dr — We ran around late at night to record wild rats in NYC and figured out how to quantify their behavior and environment. 🧵
w/ Dima Batenkov, @zamakany, @e_mackevicius
https://t.co/FbxHwbTR16
Today we’re launching AutumnBench, our benchmark built on @BasisOrg’s Autumn platform. It’s designed to measure world modeling and reasoning by placing humans and AI in unfamiliar worlds—with no rewards or guidance—to test who can figure out how these worlds actually work.
At #popl2023 w/ @MITCoCoSci@ZuckermanBrain@DataSciColumbia, we presented AutumnSynth,
AutumnSynth synthesizes the source code of a video game from seconds of play. It's a step toward automated scientific discovery through program synthesis
Blog/paper: https://t.co/XX0GvGbBfv
Introducing AutumnSynth, a step toward automating scientific discovery through program synthesis.
AutumnSynth synthesizes the source code of a video game from seconds of play.
paper: https://t.co/kDPk2kYGhn
Blog: https://t.co/cBNzCROARH
Why do this? How does it work?🧵
The final event of the Data science for social good fellowship at TU Kaiserslautern and DFKI is today.
Dial into our webinar (16:00 - 17:00 CET) to hear about the two great projects.
https://t.co/r3GIvRsCbg
Meeting ID: 674 6097 9461
Passcode: DSSGxKL22!